Context-based Image Segment Labeling (CBISL)

• URL: http://arxiv.org/abs/2011.00784v1
• Date: Mon, 2 Nov 2020 07:26:55 GMT
• Title: Context-based Image Segment Labeling (CBISL)
• Authors: Tobias Schlagenhauf, Yefeng Xia, J\"urgen Fleischer
• Abstract summary: This paper aims to recover semantic image features (objects and positions) in images. We demonstrate a new approach referred to as quadro-directional PixelCNN to recover missing objects. The results suggest that our four-directional model outperforms one-directional models.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Working with images, one often faces problems with incomplete or unclear information. Image inpainting can be used to restore missing image regions but focuses, however, on low-level image features such as pixel intensity, pixel gradient orientation, and color. This paper aims to recover semantic image features (objects and positions) in images. Based on published gated PixelCNNs, we demonstrate a new approach referred to as quadro-directional PixelCNN to recover missing objects and return probable positions for objects based on the context. We call this approach context-based image segment labeling (CBISL). The results suggest that our four-directional model outperforms one-directional models (gated PixelCNN) and returns a human-comparable performance.

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